Objectives

Turn intent into measurable action.

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Intelligence needs objectives

Objectives is an independent Hugging Face organization focused on how AI systems represent, prioritize, optimize, evaluate, and revise goals.

A capable system can generate actions.

A useful system should also understand:

What are we trying to achieve?

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The Objective Loop

INTENT
  ↓
OBJECTIVE
  ↓
CONSTRAINTS
  ↓
PLAN
  ↓
ACTION
  ↓
MEASUREMENT
  ↓
UPDATE

Objectives connect intent with behavior.

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Goal Representation

How should an AI system represent what it is trying to accomplish?

Possible topics:

• explicit goals
• subgoals
• success criteria
• priorities
• deadlines
• constraints
• preferences
• stop conditions

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Multi-Objective Optimization

Real tasks often involve competing goals.

For example:

maximize quality
minimize cost
reduce latency
preserve safety
respect constraints

There may be no single perfect answer.

A system may need to reason about trade-offs.

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Planning

Objectives become useful when they guide action.

GOAL
 ↓
SUBGOALS
 ↓
PLAN
 ↓
EXECUTION
 ↓
CHECK

Possible research areas:

• decomposition
• sequencing
• prioritization
• replanning
• resource allocation
• long-horizon planning

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Success Criteria

A goal without a measurable outcome is difficult to evaluate.

Possible questions:

• What counts as success?
• What counts as partial success?
• When should the system stop?
• Which metrics matter?
• How should trade-offs be scored?

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Objective Conflicts

AI systems may receive goals that conflict.

Example:

Objective A: maximize accuracy
Objective B: minimize latency
Objective C: minimize cost

A useful system should make these conflicts visible rather than hide them.

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Objective Updates

Goals can change during execution.

OLD OBJECTIVE
      ↓
AUTHORIZED UPDATE
      ↓
NEW OBJECTIVE
      ↓
REPLAN

This connects Objectives naturally with agents, orchestration, corrigibility, evaluation, and planning.

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Possible Spaces

Objective Builder

Turn a broad intention into structured goals, constraints, and success criteria.

Multi-Objective Planner

Compare plans across quality, cost, time, and risk.

Goal Decomposer

Break one high-level objective into measurable subgoals.

Objective Conflict Detector

Identify competing or contradictory goals.

Success Criteria Designer

Convert vague objectives into measurable evaluation criteria.

Goal Update Simulator

Test how a plan changes when an objective changes.

Pareto Explorer

Visualize trade-offs between multiple objectives.

Agent Objective Inspector

Inspect goals, priorities, constraints, and stop conditions of an agent workflow.

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Possible Datasets

Potential datasets may include:

goal-decomposition-tasks
multi-objective-scenarios
objective-conflicts
success-criteria-examples
agent-goal-traces
planning-objectives
goal-update-cases

Useful fields may include:

• objective
• priority
• constraint
• metric
• target
• subgoal
• tradeoff
• outcome
• success

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Possible Models

Models may support:

• goal extraction
• objective classification
• subgoal generation
• priority ranking
• conflict detection
• success-criteria generation
• plan scoring
• multi-objective selection

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A Simple Objective Record

{
  "objective": "Reduce inference cost",
  "constraints": [
    "quality must remain above threshold",
    "latency must stay below 2 seconds"
  ],
  "metrics": [
    "cost_per_request",
    "quality_score",
    "latency_ms"
  ],
  "success": "20% lower cost without violating constraints"
}

Clear objectives make evaluation easier.

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Objectives + Agents

Agents need goals.

A robust agent may need more than a sentence describing a task. It may need:

goal
+
priority
+
constraints
+
success criteria
+
stop conditions

That structure can make behavior easier to inspect and evaluate.

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Objectives + World Models

World models may simulate possible futures.

Objectives determine which futures are desirable.

WORLD MODEL
    ↓
POSSIBLE FUTURES
    ↓
OBJECTIVE FUNCTION
    ↓
SELECTED PLAN

Prediction tells us what might happen.

Objectives help decide what should happen.

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Objectives + Corrigibility

Objectives should not become permanently fixed.

Authorized users may need to change, narrow, replace, cancel, or constrain them.

A well-designed AI system should remain responsive to legitimate objective updates.

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Objectives + Evaluation

Evaluation asks whether a system performed well.

Objectives define what well means.

Without a clear objective, a score can be meaningless.

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Design Principles

Make goals explicit

Hidden objectives are difficult to inspect.

Separate goals from constraints

What we want and what we must not violate are different.

Define success

Every important objective should have measurable criteria where possible.

Expose trade-offs

Competing goals should be visible.

Allow updates

Objectives may change.

Evaluate outcomes

Intent matters, but results matter too.

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Who Is Objectives For?

Objectives may be useful for:

• agent developers
• AI researchers
• planning systems
• orchestration teams
• optimization researchers
• evaluation teams
• robotics developers
• enterprise AI builders
• open-source contributors

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Long-Term View

As AI systems become more capable, the difficult question may increasingly shift from:

What can the system do?

to:

What should the system optimize for?

More intelligence makes objective design more important, not less.

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Independent Organization

Objectives is an independent Hugging Face community organization.

It is not an official optimization platform, standards body, model provider, research institute, or Hugging Face organization.

The name Objectives reflects the central idea:

define what matters, make trade-offs explicit, and connect goals to measurable outcomes.

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OBJECTIVES

Align. Plan. Measure. Improve.